1071 papers with code • 4 benchmarks • 19 datasets

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )


Use these libraries to find Meta-Learning models and implementations

Most implemented papers

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

cbfinn/maml ICML 2017

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

Prototypical Networks for Few-shot Learning

jakesnell/prototypical-networks NeurIPS 2017

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

Learning to Compare: Relation Network for Few-Shot Learning

floodsung/LearningToCompare_FSL CVPR 2018

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

On First-Order Meta-Learning Algorithms

openai/supervised-reptile 8 Mar 2018

This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

google-research/meta-dataset ICLR 2020

Few-shot classification refers to learning a classifier for new classes given only a few examples.

A Closer Look at Few-shot Classification

wyharveychen/CloserLookFewShot ICLR 2019

Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

learnables/learn2learn 31 Jul 2017

In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.

Learning to Reweight Examples for Robust Deep Learning

uber-research/learning-to-reweight-examples ICML 2018

Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns.

Learning to learn by gradient descent by gradient descent

deepmind/learning-to-learn NeurIPS 2016

The move from hand-designed features to learned features in machine learning has been wildly successful.

Learning to reinforcement learn

awjuliani/Meta-RL 17 Nov 2016

We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.